r/PowerBI

Hi everyone. I am currently a final year undergrad student from Computer Science Background. I love Data Analysis of businesses or any other sort, and love making dashboards or creating data driven insights. I have knowledge of SQL and Python for data cleaning, data preprocessing and also, some statistical knowledge as well from both academic and some online courses. Moreover, I am also very much an expert in Excel and power Queries and stuffs. I also am familiar with cloud based data systems and retrieval of them using SQL or Python.

I am not yet familiar with working with very big data. I am planning a serious remote based job in PowerBI based data analyst. I am yet to master PowerBI.

I was thinking of preparing real life Case Studies on businesses that have public data, where I would generate data driven insights and show them visually using powerBI. (GPT suggested that I should do so) And document them properly. I need some advice from my seniors in this field -

1. How to rise above the entry level saturation in this field and become an intermediate level PowerBI analyst that would attract both Clients and Companies (full time remote)

2. Should I do many projects? I have found several end-to-end project based materials and tutorials on YouTube. Should I follow those to complete all projects or, Should I just do serious case studies.

3. Could you suggest a roadmap on how to achieve the level that I am targeting? I have chosen the time frame to be till the end of the year (roughly 7 months)

4. What Certifications can boost my CV for shortlisting myself for the job?

Thank you for reading all these. 😀

I'm currently working in an environment where we use on prem databases, and gen1 dataflows as the "gold layer". This to make the sources available and to be reusable. However, since this month, gen1 is legacy. The gen2 dataflows can serve as replacements, however there is an issue. When working on the company WiFi network, the new gen items ( gen2 dataflows, lakehouse...) cannot be reached. So this is problematic as you can only develop from home (new items use a new https protocol that is not allowed or something). Gen 1 is no problem, And is able to be pulled in. Self service is a very important value. How can this be solved ?

Hey everyone,

We switched from Looker Studio to Power Bi.

Everyone in our organization has a Microsoft 365 license.

Goal: I want my staff to see our reports in Power Bi

Question: If I pay for a Pro license for myself, do I need to buy a license for everyone else to view the report? I am getting mixed answers online, since usually not everyone in the organization has Microsoft365.

Thanks.

I’ve been working with Power BI recently and wanted to share a few things that helped me avoid common mistakes:

1. Always build a proper data model first Don’t jump into visuals. Relationships matter more than charts.
2. Keep measures simple and reusable Avoid writing everything in one DAX formula.
3. Use a consistent layout Helps readability and makes dashboards look more professional.
4. Focus on business questions, not just visuals A good dashboard answers something specific.
5. Start simple Don’t try to build everything at once.

Still learning, but these basics made a big difference for me. Curious what others would add to this.

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In world of Ai, Claude how do u see future of power platform developer ? I am power BI developer with 3 yrs and I am thinking to move in this role. Please suggest if it is good choice or should I continue in power bi

Thanks in advance!

I just started a new role where Power BI is part of the workflow, but I honestly have 0 practical experience with it.

I come from a finance background, so I understand some data analysis concepts in theory, but I’ve never actually built dashboards or worked hands-on with Power BI.

Right now I’m trying to catch up as fast as possible and focus on what actually matters in real use (not just theory or random tutorials).

If you’ve been in a similar situation, what would you recommend?

I’m mainly looking for a simple, practical way to learn quickly:

• what to focus on first
• what’s not worth spending time on early
• how to get comfortable building real reports/dashboards

Any advice or learning path that actually worked in practice would be appreciated.

Coming from an Industrial Management background, I’m fed up with the "Excel-as-a-database" nightmare. I just discovered Dataverse and it feels like the "missing link" for data integrity.

In 2026, with Copilots and AI Agents everywhere, can we even trust AI to reason over business data without the structure Dataverse provides? Is it worth the jump for mid-sized industrial/admin workflows, or is SharePoint still "good enough"?

Would love to hear from those who made the pivot. Is it a career-defining move or overkill?

We are going to have a map with filters applied to various tables on the report, but we also have blog posts that are related to certain tags. Is something like this possible?

I know I can just include the titles of posts with clickable URLs to the relevant content, I am just trying to figure out how fancy I can get.

to clarify, I’m not looking for a magic button here, I write dax daily and know its nuances. Just curious which model (gpt 5.4, claude 4.6) has been the most reliable for you in real projects.

which llm have you found most effective at understanding complicated dax logic (especially with multiple table relationships, context transition, iterators) and generating the fewest syntax errors?

Thanks!

Hi everyone! I've recently began my powerbi journy coming from a background of heavy Sql and power query work in excel. As the more technical individual in my finance org I've been tasked explore and revamp our reporting through PowerBI. Due to differing data granularity I have 2 fact tables that switch between actuals and budget however I focused on keeping normalized 1:m relationships.

As finance orgs do leadership wants the same matrix report accessible in excel however I'm finding the cubevalue functions I'm creating (converted from power pivots) to be painfully slow when modified via slicer. Does anyone have recommendations on how to optimize? The underlying dax measures use switch statements and conditional logic to define certain KPIs. I've explored Dax studio and seems like the main bottleneck is large FE costs.

Within powerbi at most matrices take 1 sec to load but in excel it's up to minutes.

Are there specific limitations with this use case of trying to build reports connected to powerbi semantic model? Leadership is stuck on having the powerbi semantic model be the gold source of truth but I've built similar reports in excels data model that run way faster with data ingested directly from our database

Would love to hear if anyone else has created large cubevalue reports ine excel. Any insight is appreciated!!

Also our shared on prem drive is to no longer be used.

With our current setup, I feel like all that is happening is now I have an extra step where I have to also save my PBIX in SharePoint.

Is there something else I can do to make this more of a positive change that I’m missing?

We have power apps & currently PBI pro licensing. If you think any apps or upgrades will be necessary for a decent workflow let me know and also a poor man’s version of they get sticker shock.

I primarily use SSMS, power query and Power BI Desktop. Our data is on-prem, but they will eventually move to azure most likely.

Matrix visual:

Entity	Value1	Value2	MetricColumnCalc
A	598	1513.8836	0.16
B	256	679	0.32

Value 1 and 2 are in my source data, and are simple sums when placing in matrix. Value 1 / Value 2 needs to be the MetricColumnCalc answer, but I am getting weird results. I am using the divide function for this. When I do Value1/Value2 - I get NaN as Value 1 and Value 2 can be zeros. I switched to Divide (value1/value2,0) but I am afraid that 0 is creating more 0s to calculate, creating a lower than intended score. Maybe I need to keep my blanks as blanks and not 0?

For example, 598/1513.8836=0.395 (not 0.16). Value 1 and 2 are both constant, in every row. Granted this is summarized, and I tried sumx/divide/etc., but it seems like such a simple calculation but is creating headaches.

Hi,

I’m a student who needs to learn how to make dashboards with PowerBi.

I think powerbi is complicated, so does anyone who have some video reccomondations or helpfull guides I could use?

Thank u!

My org is in the process of building out a data warehouse, and most of the semantic model work is being handled by BI. I’ve been tasked with building out some “datamarts” (not even sure that’s the right term), and I’m trying to make sure I’m structuring things the right way.

Background:

• We’re coming from Tableau, where most data sources are basically wide/flat tables
• No one on my team has much experience with proper semantic modeling yet
• The example I was given for a “datamart” looks very similar to a Tableau-style dataset (80+ columns)

The scenario:

I have an account-level fact table (1 row per account) with financial/sales data.

Each account is associated with 3 different employee roles:

• Sales Rep
• Account Manager
• Support Rep

For each of those roles, we currently have ~5 fields:

• Name
• ID
• Department
• Territory
• etc.

So in total, that’s ~15–18 employee-related columns sitting in the same table.

Instead of keeping all those columns in the account table, I’m thinking:

• Keep only:
  • Sales_Rep_ID
  • Account_Manager_ID
  • Support_Rep_ID
• Then have a separate Employee dimension table with:
  • Employee_ID
  • Name
  • Department
  • Territory
  • etc.

I believe this is called a role-playing dimension setup

Questions:

1. Is this the right approach? (i.e., moving employee attributes out of the fact table into a shared dimension)
2. How does this typically get exposed to end users in Power BI? For example, would I be able to present fields like:
   • Sales Rep Territory
   • Account Manager Territory
   • Support Rep Territory
   • even though they all come from the same underlying employee table?
3. From a usability standpoint, how would you organize this?
   • One big “Employees” folder with fields like:
     • Sales Rep Territory
     • Account Manager Territory
   • Or separate folders:
     • Sales Rep
     • Account Manager
     • Support Rep
     • With separate folders, is it possible to have them as subfolders under "Employees"?

Really trying to strike the right balance between:

• clean modeling
• and a UI that actually makes sense to analysts

Hello everyone,

Just thought I'd share a bit of a coding adventure.

Measure Killer is a pretty great tool that allows you to do impact analysis on your semantic models.

It allows you to find out where each measure is used in each report down to the visual name. It can do a lot more than that, but I'm particularly a fan of this specific feature.

The problem is that the free version of Measure Killer is rather limited : you can't analyse reports that use Analysis Services for the semantic model.

For this reason, I set out to understand how a PowerBI Semantic model works, and create my own little tool for impact analysis.

The result is a small project called FindMyMeasure. It is a tool built with the dotNet Framework that allow the user to choose one or more pbix report, and start an analysis. The result is a list of measures, columns and hierarchies, with their usage level (Used, Unused and Used by Unused)

FindMyMeasure currently supports analysing Power BI reports that are connected in Live connection to an Analysis Services Tabular Model, and reports that are just in Import mode.

https://preview.redd.it/y8jj7lbsudwg1.png?width=1918&format=png&auto=webp&s=38a6711e8958c07711044224f9559ac27bcd812a

It still lacks a bunch of features, as well as some unit testing, but it fits my use case for now.

It is still very much a work in progress. I'm not a professional software engineer, and I do coding as a hobby.

Here is the code if you want to look into it :

https://github.com/devinou971/FindMyMeasure

It's target vs sales .

We have an Azure SQL VM that hosts our data as well as our on‑premises Power BI gateway. I reboot this VM regularly (about once a month), and historically this has never caused any problems.

However, after a reboot a few days ago, all of our Power BI Service refreshes have become extremely slow or fail entirely. During a refresh, the VM’s CPU spikes to nearly 100%, making the server unusable.

I’ve already tried:

•	Multiple additional reboots

•	Excluding relevant folders/files from Microsoft Defender scans

•	Updating the gateway to the latest version

Unfortunately, none of these steps have improved refresh times or CPU utilization.

Has anyone experienced something similar or have ideas on what might have changed after the reboot? I’m trying to understand what could have caused this sudden performance issue when previous reboots never caused any trouble and restore the performance to how it was before this latest reboot.

Environment
• Power BI Desktop + Power BI Service
• On-premises SQL Server via On-Premises Data Gateway
• Import mode (no DirectQuery, no incremental refresh, no RLS)

Problem
When I trigger a full dataset refresh in Power BI Service or in power bi desktop, the refresh status shows "Completed" with no errors, but the report still shows old data. New rows added to SQL Server tables do not appear, but if i refresh table by table in power bi desktop i get new data.

What I already verified
• Gateway connection is working, I created a test table in SQL Server with 3 rows, published a new report, added a 4th row, refreshed in Service → the new row appeared correctly. So the gateway and data source mapping are confirmed working.
• No incremental refresh configured on any table.
• No Row Level Security (RLS).
• The report is connected to the correct dataset.
• All DAX warnings and errors have been fixed (previously had missing tables, missing columns, and a SWITCH type mismatch — all resolved).
• Disabling parallel table loading had no effect.

What I found during investigation
Some tables had "View Native Query" greyed out in Power Query, meaning query folding was broken. 

For one table I removed the Table.AddColumn step and replaced the logic with a DAX calculated column this worked but the approach is not practical for other tables that have many complex steps I cannot easily change or remove.

I also tried adding CommandTimeout=#duration(0,1,0,0) to the source.

I’ve been experimenting with combining AI tools (specifically Claude) with Power BI semantic models, and it’s starting to feel like a meaningful shift in how analytics might be consumed.

Traditionally, the workflow looks something like this:

• Build reports and dashboards
• Add visuals to answer anticipated questions
• Users click, filter, and explore

That model works well, but it’s always limited by what was pre-built into the report.

What’s interesting now is the ability to connect an LLM directly to a semantic model and let users ask questions in natural language. Instead of navigating visuals, they can just ask things like:

• Why did June outperform March?
• What are the main drivers behind this trend?
• How do two entities compare and what should we do about it?

The responses aren’t just filtered views of existing visuals either. The model can:

• Pull in dimensions not surfaced in the report
• Provide summaries across multiple metrics
• Offer explanations and even recommendations

It starts to feel less like “report consumption” and more like interacting with the data itself.

A few things that stood out to me while testing this:

• The semantic model becomes way more important than the report layer
• You can’t realistically pre-build all the insights users might want
• AI fills the gap between high-level dashboards and deep analysis
• There’s real potential for scaling analytics to less technical users

That said, there are still some open questions:

• How do you manage trust and validation of AI-generated insights?
• What does governance look like when users can query anything?
• Does this reduce the need for complex report design, or just shift it?
• How do organizations handle access and security at scale?

Curious how others are thinking about this.

Are you exploring AI + Power BI integrations yet?
Do you see this replacing parts of the dashboard experience, or just augmenting it?